{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee7447a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ee9c457",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据\n",
    "file_path='F:\\大二下\\数据分析与可视化\\\\EducationData.xlsx'\n",
    "lr_data=pd.read_excel(file_path,'Literacy rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "609971d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Countries and areas</th>\n",
       "      <th>Development Regions</th>\n",
       "      <th>Total</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Age Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>65.420547</td>\n",
       "      <td>56.254749</td>\n",
       "      <td>74.084801</td>\n",
       "      <td>youth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>More Developed</td>\n",
       "      <td>99.330002</td>\n",
       "      <td>99.629997</td>\n",
       "      <td>99.050003</td>\n",
       "      <td>youth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>97.426521</td>\n",
       "      <td>97.252159</td>\n",
       "      <td>97.594063</td>\n",
       "      <td>youth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>More Developed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Countries and areas Development Regions      Total     Female       Male  \\\n",
       "0         Afghanistan     Least Developed  65.420547  56.254749  74.084801   \n",
       "1             Albania      More Developed  99.330002  99.629997  99.050003   \n",
       "2             Algeria      Less Developed  97.426521  97.252159  97.594063   \n",
       "3             Andorra      More Developed        NaN        NaN        NaN   \n",
       "4              Angola     Least Developed        NaN        NaN        NaN   \n",
       "\n",
       "  Age Range  \n",
       "0     youth  \n",
       "1     youth  \n",
       "2     youth  \n",
       "3       NaN  \n",
       "4       NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21190014",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 404 entries, 0 to 403\n",
      "Data columns (total 6 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Countries and areas  404 non-null    object \n",
      " 1   Development Regions  404 non-null    object \n",
      " 2   Total                156 non-null    float64\n",
      " 3   Female               156 non-null    float64\n",
      " 4   Male                 156 non-null    float64\n",
      " 5   Age Range            156 non-null    object \n",
      "dtypes: float64(3), object(3)\n",
      "memory usage: 19.1+ KB\n"
     ]
    }
   ],
   "source": [
    "#不同国家青少年和成人识字能力的人数\n",
    "lr_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba814b1c",
   "metadata": {},
   "source": [
    "### 3.1对识字率缺失值分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "613bb5a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Countries and areas      0\n",
       "Development Regions      0\n",
       "Total                  248\n",
       "Female                 248\n",
       "Male                   248\n",
       "Age Range              248\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing1 = lr_data.isnull().sum()\n",
    "missing1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f165269c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除前数据总量:  (404, 6)\n"
     ]
    }
   ],
   "source": [
    "missing3 = lr_data.isnull().sum()\n",
    "print('删除前数据总量: ',lr_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b400528",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 3000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用missingno库进行可视化展示\n",
    "import missingno\n",
    "# 使用missingno库进行可视化展示\n",
    "missingno.matrix(lr_data,figsize=(30,5)) #以矩阵的形式\n",
    "#以条形图的方式查看\n",
    "missingno.bar(lr_data,sort='ascending',figsize=(30,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "46f6139e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Countries and areas    0\n",
      "Development Regions    0\n",
      "Total                  0\n",
      "Female                 0\n",
      "Male                   0\n",
      "Age Range              0\n",
      "dtype: int64\n",
      "删除后数据总量:  (156, 6)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 156 entries, 0 to 402\n",
      "Data columns (total 6 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Countries and areas  156 non-null    object \n",
      " 1   Development Regions  156 non-null    object \n",
      " 2   Total                156 non-null    float64\n",
      " 3   Female               156 non-null    float64\n",
      " 4   Male                 156 non-null    float64\n",
      " 5   Age Range            156 non-null    object \n",
      "dtypes: float64(3), object(3)\n",
      "memory usage: 8.5+ KB\n",
      "删除后数据:  None\n"
     ]
    }
   ],
   "source": [
    "lr_data.dropna(how='any', inplace=True)\n",
    "print(lr_data.isna().sum())\n",
    "print('删除后数据总量: ',lr_data.shape)\n",
    "print('删除后数据: ',lr_data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c88ee8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#缺失值处理后\n",
    "lr_data.to_excel(r'F:\\大二下\\数据分析与可视化\\中间数据\\\\result_3.xlsx',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a117e75",
   "metadata": {},
   "source": [
    "### 3.2对识字率重复值分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3a4b8213",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      Afghanistan\n",
      "1          Albania\n",
      "2          Algeria\n",
      "7        Argentina\n",
      "13         Bahrain\n",
      "          ...     \n",
      "396        Uruguay\n",
      "397     Uzbekistan\n",
      "398        Vanuatu\n",
      "400       Viet Nam\n",
      "402         Zambia\n",
      "Name: Countries and areas, Length: 156, dtype: object\n",
      "重复值个数为: 0\n"
     ]
    }
   ],
   "source": [
    "#Countries and areas\n",
    "\n",
    "duplicates=lr_data['Countries and areas'][lr_data['Countries and areas'].duplicated(keep=False)]\n",
    "print(duplicates)\n",
    "print(\"重复值个数为:\",lr_data.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f510a0",
   "metadata": {},
   "source": [
    "### 3.3对识字率异常值分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "effaf821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制箱型图\n",
    "lr_data.boxplot(column=['Total', 'Female', 'Male'], by='Age Range')  # 分年龄范围绘制箱型图\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3352599a",
   "metadata": {},
   "source": [
    "### 3.4数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4709586b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>87.641465</td>\n",
       "      <td>85.861241</td>\n",
       "      <td>89.437676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.464298</td>\n",
       "      <td>20.221508</td>\n",
       "      <td>14.815110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>34.522758</td>\n",
       "      <td>25.735161</td>\n",
       "      <td>40.264809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>81.924833</td>\n",
       "      <td>80.194101</td>\n",
       "      <td>85.253447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>95.763134</td>\n",
       "      <td>95.530434</td>\n",
       "      <td>96.681141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>99.096601</td>\n",
       "      <td>99.185755</td>\n",
       "      <td>99.051741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total      Female        Male\n",
       "count  156.000000  156.000000  156.000000\n",
       "mean    87.641465   85.861241   89.437676\n",
       "std     17.464298   20.221508   14.815110\n",
       "min     34.522758   25.735161   40.264809\n",
       "25%     81.924833   80.194101   85.253447\n",
       "50%     95.763134   95.530434   96.681141\n",
       "75%     99.096601   99.185755   99.051741\n",
       "max    100.000000  100.000000  100.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7a658af3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          Female         Male\n",
      "Development Regions                          \n",
      "Least Developed      2523.132982  2882.878162\n",
      "Less Developed       8496.374615  8691.346779\n",
      "More Developed       2374.845985  2378.052559\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(lr_data)\n",
    "# 创建数据透视表，以国家发展程度为索引，计算男女识字率\n",
    "pivot = df.pivot_table(index='Development Regions', values=['Male','Female'], aggfunc='sum') \n",
    "# 输出结果\n",
    "print(pivot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b826e7b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Total\n",
      "Age Range             \n",
      "adult      6572.457073\n",
      "youth      7099.611465\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(lr_data)\n",
    "# 创建数据透视表，以不同年龄段为索引，计算总识字率\n",
    "pivot_lrf = df.pivot_table(index='Age Range', values='Total', aggfunc='sum')\n",
    "print(pivot_lrf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6a7cb60c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          Female\n",
      "Development Regions             \n",
      "Least Developed      2523.132982\n",
      "Less Developed       8496.374615\n",
      "More Developed       2374.845985\n",
      "                            Male\n",
      "Development Regions             \n",
      "Least Developed      2882.878162\n",
      "Less Developed       8691.346779\n",
      "More Developed       2378.052559\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(lr_data)\n",
    "\n",
    "# 创建数据透视表，以经济状态为行索引，男女识字率为列，计算总识字率\n",
    "pivot_lrf = df.pivot_table(index='Development Regions', values='Female', aggfunc='sum')\n",
    "pivot_lrm = df.pivot_table(index='Development Regions', values='Male', aggfunc='sum')\n",
    "print(pivot_lrf)\n",
    "print(pivot_lrm)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
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 },
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